用分层卷积融合多尺度信息，提升遥感图像语义分割精度
TNet: Terrace Convolutional Decoder Network for Remote Sensing Image Semantic Segmentation
- 通过逐级融合低分辨率全局特征与高分辨率局部特征
- 在三个数据集上实现85.35%~87.05%的mIoU，效率高
- 仅用卷积和加法操作，适合资源受限场景
在遥感图像语义分割中，多数网络采用UNet架构，并引入Transformer或Mamba模块以增强解码阶段的全局-局部特征交互。然而，这些方法通常关注单尺度内关系，忽视跨分辨率的全局上下文依赖。为此，本文提出分层卷积解码器网络（TNet），仅使用卷积和加法操作，逐步将富含全局上下文的低分辨率特征融合到富含局部细节的高分辨率特征中。该渐进式融合使模型能学习空间感知的卷积核，在各解码阶段自然融合全局与局部信息。我们基于ResNet-18编码器实现TNet-R，在三个基准数据集上评估：在ISPRS Vaihingen上达到85.35%的mIoU，Potsdam上为87.05%，LoveDA上为52.19%，同时保持高计算效率。代码已公开。
原文摘要 · Abstract (English)
In remote sensing, most segmentation networks adopt the UNet architecture, often incorporating modules such as Transformers or Mamba to enhance global-local feature interactions within decoder stages. However, these enhancements typically focus on intra-scale relationships and neglect the global contextual dependencies across multiple resolutions. To address this limitation, we introduce the Terrace Convolutional Decoder Network (TNet), a simple yet effective architecture that leverages only convolution and addition operations to progressively integrate low-resolution features (rich in global context) into higher-resolution features (rich in local details) across decoding stages. This progressive fusion enables the model to learn spatially-aware convolutional kernels that naturally blend global and local information in a stage-wise manner. We implement TNet with a ResNet-18 encoder (TNet-R) and evaluate it on three benchmark datasets. TNet-R achieves competitive performance with a mean Intersection-over-Union (mIoU) of 85.35\% on ISPRS Vaihingen, 87.05\% on ISPRS Potsdam, and 52.19\% on LoveDA, while maintaining high computational efficiency. Code is publicly available.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。